Many‐Objective Optimization Using Adaptive Differential Evolution with a New Ranking Method

Abstract

Pareto dominance is an important concept and is usually used in multiobjective evolutionary algorithms (MOEAs) to determine the nondominated solutions. However, for many‐objective problems, using Pareto dominance to rank the solutions even in the early generation, most obtained solutions are often the nondominated solutions, which results in a little selection pressure of MOEAs toward the optimal solutions. In this paper, a new ranking method is proposed for many‐objective optimization problems to verify a relatively smaller number of representative nondominated solutions with a uniform and wide distribution and improve the selection pressure of MOEAs. After that, a many‐objective differential evolution with the new ranking method (MODER) for handling many‐objective optimization problems is designed. At last, the experiments are conducted and the proposed algorithm is compared with several well‐known algorithms. The experimental results show that the proposed algorithm can guide the search to converge to the true PF and maintain the diversity of solutions for many‐objective problems.